```
# for QR codes use inline
# %matplotlib inline
# qr_setting = 'url'
# qrviz_setting = 'show'
#
# for lecture use notebook
%matplotlib inline
qr_setting = None
#
%config InlineBackend.figure_format='retina'
# import libraries
import numpy as np
import matplotlib as mp
import pandas as pd
import matplotlib.pyplot as plt
import laUtilities as ut
import slideUtilities as sl
import demoUtilities as dm
import pandas as pd
from importlib import reload
from datetime import datetime
from IPython.display import Image
from IPython.display import display_html
from IPython.display import display
from IPython.display import Math
from IPython.display import Latex
from IPython.display import HTML;
```

# Linear Transformations¶

```
display(Image("images/L7 F3.jpg", width=650))
```

So far we’ve been treating the matrix equation

as simply another way of writing the vector equation

However, we’ll now think of the matrix equation in a new way:

We will think of \(A\) as ”acting on” the vector \({\bf x}\) to create a new vector \({\bf b}\).

For example, let’s let \(A = \left[\begin{array}{rrr}2&1&1\\3&1&-1\end{array}\right].\) Then we find:

In other words, if \({\bf x} = \left[\begin{array}{r}1\\-4\\-3\end{array}\right]\) and \({\bf b} = \left[\begin{array}{r}-5\\2\end{array}\right]\), then \(A\) *transforms* \({\bf x}\) into \({\bf b}\).

```
display(Image("images/L7 F1.jpg", width=650))
```

Notice what \(A\) has done: it took a vector in \(\mathbb{R}^3\) and transformed it into a vector in \(\mathbb{R}^2\).

How does this fact relate to the shape of \(A\)?

\(A\) is \(2 \times 3\) — that is, \(A \in \mathbb{R}^{2\times 3}\).

This gives a **new** way of thinking about solving \(A{\bf x} = {\bf b}\).

To solve \(A{\bf x} = {\bf b}\), we much “search for” the vector(s) \({\bf x}\) in \(\mathbb{R}^3\) that are transformed into \({\bf b}\) in \(\mathbb{R}^2\) under the “action” of \(A\).

For a different \(A\), the mapping might be from \(\mathbb{R}^1\) to \(\mathbb{R}^3\):

```
display(Image("images/L7 F2.jpg", width=650))
```

What would the shape of \(A\) be in the above case?

Since \(A\) maps from \(\mathbb{R}^1\) to \(\mathbb{R}^3\), \(A \in \mathbb{R}^{3\times 1}\).

That is, \(A\) has 3 rows and 1 column.

In another case, \(A\) could be a square \(2\times 2\) matrix.

Then, it would map from \(\mathbb{R}^2\) to \(\mathbb{R}^2\):

```
display(Image("images/L7 F3.jpg", width=650))
```

We have moved out of the familiar world of functions of one variable: we are now thinking about functions that transform a vector into a vector.

Or, put another way, functions that transform multiple variables into multiple variables.

## Transformations¶

Some terminology:

A **transformation** (or **function** or **mapping**) \(T\) from \(\mathbb{R}^n\) to \(\mathbb{R}^m\) is a rule that assigns to each vector \({\bf x}\) in \(\mathbb{R}^n\) a vector \(T({\bf x})\) in \(\mathbb{R}^m\).

The set \(\mathbb{R}^n\) is called the **domain** of \(T\), and \(\mathbb{R}^m\) is called the **codomain** of \(T\).

The notation:

indicates that the domain of \(T\) is \(\mathbb{R}^n\) and the codomain is \(\mathbb{R}^m\).

For \(\bf x\) in \(\mathbb{R}^n,\) the vector \(T({\bf x})\) is called the **image** of \(\bf x\) (under \(T\)).

The set of all images \(T({\bf x})\) is called the **range** of \(T\).

```
# image credit: Lay, 4th edition
display(Image("images/L7 F4.jpg", width=650))
```

Here, the green plane is the set of all points that are possible outputs of \(T\) for some input \(\mathbf{x}\).

So in this example:

The

**domain**of \(T\) is \(\mathbb{R}^2\)The

**codomain**of \(T\) is \(\mathbb{R}^3\)The

**range**of \(T\) is the green plane.

Question Time! Q7.1

Let’s do an example. Let’s say I have these points in \(\mathbb{R}^2\):

Where are these points located?

```
square = np.array([[0, 1, 1, 0],
[1, 1, 0, 0]])
dm.plotSetup()
print(square)
dm.plotSquare(square)
```

```
[[0 1 1 0]
[1 1 0 0]]
```

Now let’s transform each of these points according to the following rule. Let

We define \(T({\bf x}) = A{\bf x}\). Then we have

What is the image of each of these points under \(T\)?

```
ax = dm.plotSetup()
print("square = "); print(square)
dm.plotSquare(square)
#
# create the A matrix
A = np.array([[1.0, 1.5],[0.0,1.0]])
print("A matrix = "); print(A)
#
# apply the shear matrix to the square
ssquare = np.zeros(np.shape(square))
for i in range(4):
ssquare[:,i] = dm.AxVS(A,square[:,i])
print("sheared square = "); print(ssquare)
dm.plotSquare(ssquare,'r')
```

```
square =
[[0 1 1 0]
[1 1 0 0]]
A matrix =
[[1. 1.5]
[0. 1. ]]
sheared square =
[[1.5 2.5 1. 0. ]
[1. 1. 0. 0. ]]
```

This sort of transformation, where points are successively slid sideways, is called a **shear** transformation.

## Linear Transformations¶

By the properties of matrix-vector multiplication, we know that the transformation \({\bf x} \mapsto A{\bf x}\) has the properties that

for all \(\bf u, v\) in \(\mathbb{R}^n\) and all scalars \(c\).

We are now ready to define one of the most fundamental concepts in the course: the concept of a *linear transformation*.

(You are now finding out why the subject is called linear algebra!)

**Definition.** A transformation \(T\) is **linear** if:

\(T({\bf u} + {\bf v}) = T({\bf u}) + T({\bf v}) \;\;\;\) for all \(\bf u, v\) in the domain of \(T\); and

\(T(c{\bf u}) = cT({\bf u}) \;\;\;\) for all scalars \(c\) and all \(\bf u\) in the domain of \(T\).

To fully grasp the significance of what a linear transformation is, don’t think of just matrix-vector multiplication. Think of \(T\) as a function in more general terms.

The definition above captures a *lot* of transformations that are not matrix-vector multiplication. For example, think of:

Is \(T\) a linear transformation?

## Properties of Linear Transformations¶

A key aspect of a linear transformation is that it **preserves the operations of vector addition and scalar multiplication.**

For example: for vectors \(\mathbf{u}\) and \(\mathbf{v}\), one can either:

Transform them both according to \(T()\), then add them, or:

Add them, and then transform the result according to \(T()\).

One gets the same result either way. The transformation does not affect the addition.

This leads to two important facts.

If \(T\) is a linear transformation, then

and

In fact, if a transformation satisfies the second equation for all \(\mathbf{u}, \mathbf{v}\) and \(c, d,\) then it must be a linear transformation.

Both of the rules defining a linear transformation derive from this single equation.

**Example.**

Given a scalar \(r\), define \(T: \mathbb{R}^2 \rightarrow \mathbb{R}^2\) by \(T(\mathbf{x}) = r\mathbf{x}\).

(\(T\) is called a **contraction** when \(0\leq r \leq 1\) and a **dilation** when \(r > 1\).)

Let \(r = 3\), and show that \(T\) is a linear transformation.

**Solution.**

Let \(\mathbf{u}, \mathbf{v}\) be in \(\mathbb{R}^2\) and let \(c, d\) be scalars. Then

Thus \(T\) is a linear transformation because it satisfies the rule \(T(c\mathbf{u} + d\mathbf{v}) = cT(\mathbf{u}) + dT(\mathbf{v})\).

**Example.**

Let \(T(\mathbf{x}) = \mathbf{x} + \mathbf{b}\) for some \(\mathbf{b} \neq 0\).

What sort of operation does \(T\) implement?

Answer: **translation.**

Is \(T\) a linear transformation?

**Solution.**

We only need to compare

to

So:

and

If \(\mathbf{b} \neq 0\), then the above two expressions are not equal.

So \(T\) is **not** a linear transformation.

Question Time! Q7.2

### A Non-Geometric Example: Manufacturing¶

A company manufactures two products, B and C. To do so, it requires materials, labor, and overhead.

For one dollar’s worth of product B, it spends 45 cents on materials, 25 cents on labor, and 15 cents on overhead.

For one dollar’s worth of product C, it spends 40 cents on materials, 30 cents on labor, and 15 cents on overhead.

Let us construct a “unit cost” matrix:

Let \(\mathbf{x} = \left[\begin{array}{r}x_1\\x_2\end{array}\right]\) be a production vector, corresponding to \(x_1\) dollars of product B and \(x_2\) dollars of product C.

Then define \(T: \mathbb{R}^2 \rightarrow \mathbb{R}^3\) by

The mapping \(T\) transforms a list of production quantities into a list of total costs.

The linearity of this mapping is reflected in two ways:

If production is increased by a factor of, say, 4, ie, from \(\mathbf{x}\) to \(4\mathbf{x}\), then the costs increase by the same factor, from \(T(\mathbf{x})\) to \(4T(\mathbf{x})\).

If \(\mathbf{x}\) and \(\mathbf{y}\) are production vectors, then the total cost vector associated with combined production of \(\mathbf{x} + \mathbf{y}\) is precisely the sum of the cost vectors \(T(\mathbf{x})\) and \(T(\mathbf{y})\).